{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['test_loss', 'train_loss', 'complexity', 'equation', 'test_preds', 'train_preds', 'nll', 'train_x', 'train_y', 'test_x', 'test_y'])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4z/0371zfqj5mx0rlct4r3qclfm0000gn/T/ipykernel_88427/3016424578.py:64: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.\n",
      "  for legobj in leg.legendHandles:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import defaultdict\n",
    "\n",
    "sns.set(style=\"whitegrid\", font_scale=1.3)\n",
    "\n",
    "pkl_fn = \"../precomputed_outputs/simplicity_bias/simplicity_bias_results.pkl\"\n",
    "with open(pkl_fn, 'rb') as f:\n",
    "    results = pickle.load(f)\n",
    "\n",
    "print(results.keys())\n",
    "test_losses = results['test_loss']\n",
    "nlls = results['nll']\n",
    "complexities = results['complexity']\n",
    "train_losses = results['train_loss']\n",
    "equations = results['equation']\n",
    "train_preds = results['train_preds']\n",
    "test_preds = results['test_preds']\n",
    "\n",
    "train_x = results['train_x']\n",
    "train_y = results['train_y']\n",
    "test_x = results['test_x']\n",
    "test_y = results['test_y']\n",
    "\n",
    "test_losses = np.array(test_losses)\n",
    "test_losses = (test_losses - test_losses.min()) / (test_losses.max() - test_losses.min())\n",
    "nlls = np.array(nlls)\n",
    "nlls = (nlls - nlls.min()) / (nlls.max() - nlls.min())\n",
    "complexities = np.array(complexities)\n",
    "complexities = (complexities - complexities.min()) / (complexities.max() - complexities.min())\n",
    "train_losses = np.array(train_losses)\n",
    "train_losses = (train_losses - train_losses.min()) / (train_losses.max() - train_losses.min())\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(2, 2))\n",
    "ax.plot(train_losses, label='Train Loss', color='black', linewidth=1.)\n",
    "ax.plot(complexities, label='Complexity', color='tab:brown', linewidth=1.)\n",
    "ax.plot(test_losses, label='Test Loss', color='blue', linewidth=1.)\n",
    "ax.plot(nlls, label='LLM NLL', color='tab:cyan', linewidth=2.)\n",
    "# ax.set_ylim(0, 500)\n",
    "\n",
    "ax.set_ylabel(\"Normalized Value\", rotation=270, labelpad=18, fontsize=14)\n",
    "#reduce y tick label pad\n",
    "ax.tick_params(axis='y', which='major', pad=0)\n",
    "\n",
    "#move y labels to the right\n",
    "ax.yaxis.tick_right()\n",
    "ax.yaxis.set_label_position(\"right\")\n",
    "\n",
    "# add two column legend above the figure\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "leg = fig.legend(\n",
    "    handles, labels,\n",
    "    ncol=2,\n",
    "    bbox_to_anchor=(1.2, -0.1),#1.08),\n",
    "    columnspacing=0.5,\n",
    "    handletextpad=0.5,\n",
    "    handlelength=1.0,\n",
    "    fontsize=12,\n",
    ")\n",
    "\n",
    "# set the linewidth of each legend object\n",
    "for legobj in leg.legendHandles:\n",
    "    legobj.set_linewidth(1.5)\n",
    "\n",
    "#add xlabels for [1,2,3,4,5]\n",
    "ax.set_xticks([0, 1, 2, 3, 4])\n",
    "ax.set_xticklabels([1, 2, 3, 4, 5])\n",
    "\n",
    "#turn off grid lines\n",
    "ax.grid(False)\n",
    "\n",
    "fig.savefig(\"outputs/simplicity_bias_plot.svg\", bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['(x0 + 0.3652524)',\n",
       " 'cos(cos(x0 / -0.031412385) * (-1.5252972 + x0))',\n",
       " '(sin(cos(cos(x0 / 0.031470172) * -1.4668792)) + (cos(0.81965065) * x0))',\n",
       " '(sin(cos(cos((x0 / sin(-0.03127001)) + 0.07646165) * -1.4539052)) + (sin(sin(cos(cos(exp(cos(-0.03127001) + x0))))) * x0))',\n",
       " '(cos((cos((x0 / -0.03127001) + 0.07646165) / -0.957405) / sin(sin(cos(x0 - x0)) * exp(cos(sin(x0 / -0.983214))))) / (cos(sin(sin(sin(sin(x0)) - (x0 * (-0.47036648 - (x0 / 0.5857117)))))) - -0.10476875))']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "equations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arima\n",
      "TCN\n",
      "text-davinci-003\n",
      "gp\n",
      "arima\n",
      "TCN\n",
      "N-BEATS\n",
      "N-HiTS\n",
      "text-davinci-003\n",
      "gp\n",
      "arima\n",
      "TCN\n",
      "N-BEATS\n",
      "N-HiTS\n",
      "text-davinci-003\n",
      "gp\n",
      "arima\n",
      "TCN\n",
      "N-BEATS\n",
      "N-HiTS\n",
      "text-davinci-003\n",
      "gp\n",
      "arima\n",
      "TCN\n",
      "N-BEATS\n",
      "N-HiTS\n",
      "text-davinci-003\n",
      "gp\n",
      "arima\n",
      "TCN\n",
      "N-BEATS\n",
      "N-HiTS\n",
      "text-davinci-003\n",
      "gp\n",
      "arima\n",
      "TCN\n",
      "N-BEATS\n",
      "N-HiTS\n",
      "text-davinci-003\n",
      "gp\n",
      "arima\n",
      "TCN\n",
      "N-BEATS\n",
      "N-HiTS\n",
      "text-davinci-003\n",
      "gp\n",
      "arima\n",
      "TCN\n",
      "N-BEATS\n",
      "N-HiTS\n",
      "text-davinci-003\n",
      "arima\n",
      "arima\n",
      "TCN\n",
      "text-davinci-003\n",
      "gp\n",
      "arima\n",
      "TCN\n",
      "N-BEATS\n",
      "N-HiTS\n",
      "text-davinci-003\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "from data.synthetic import get_synthetic_datasets\n",
    "from data.metrics import calculate_crps\n",
    "\n",
    "datasets = get_synthetic_datasets()\n",
    "nlls = defaultdict(dict)\n",
    "crps = defaultdict(dict)\n",
    "mae = defaultdict(dict)\n",
    "\n",
    "for dsname,(train,test) in datasets.items():\n",
    "    with open(f'../precomputed_outputs/synthetic/{dsname}.pkl','rb') as f:\n",
    "        data_dict = pickle.load(f)\n",
    "\n",
    "    for model_name,preds in data_dict.items():\n",
    "        print(model_name)\n",
    "        try: \n",
    "            if 'NLL/D' not in preds:\n",
    "                continue\n",
    "            nll = preds['NLL/D']\n",
    "            if model_name=='text-davinci-003-tuned':\n",
    "                model_name='GPT3'\n",
    "            nlls[model_name][dsname] = nll\n",
    "            test_values = test.values if isinstance(test,pd.Series) else test\n",
    "\n",
    "            crps[model_name][dsname] = calculate_crps(test_values,preds['samples'].values[:10],5)\n",
    "            tmae = np.abs(test_values-preds['median'].values).mean()/np.abs(test_values).mean()\n",
    "            mae[model_name][dsname] = tmae\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "        \n",
    "data = []\n",
    "\n",
    "for model_name in nlls:\n",
    "    for dsname in nlls[model_name]:\n",
    "        entry = {\n",
    "            \"model_name\": model_name,\n",
    "            \"dataset_name\": dsname,\n",
    "            \"nll\": nlls[model_name][dsname],\n",
    "            \"mae\": mae[model_name][dsname],\n",
    "            \"crps\": crps[model_name][dsname],\n",
    "        }\n",
    "        data.append(entry)\n",
    "\n",
    "# Convert the list of dictionaries to a DataFrame\n",
    "result_df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['exp', 'x_times_sine', 'linear', 'log', 'beat', 'square',\n",
       "       'gaussian_wave', 'sigmoid', 'sine', 'sinc', 'xsin', 'linear_cos'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df = result_df[result_df['model_name'].isin(['arima', 'TCN', 'text-davinci-003'])]\n",
    "result_df['dataset_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4z/0371zfqj5mx0rlct4r3qclfm0000gn/T/ipykernel_88427/3788039766.py:108: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n",
      "  plt.subplots_adjust(wspace=0, hspace=.5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x350 with 33 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_datasets = len(datasets)\n",
    "n_cols = 11\n",
    "n_rows = 3#(n_datasets + n_cols - 1) // n_cols\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\", font_scale=1.3)\n",
    "fig, axes = plt.subplots(\n",
    "    n_rows, n_cols, \n",
    "    figsize=(8, 3.5), constrained_layout=True, #sharex=True,\n",
    "    gridspec_kw={'width_ratios': [\n",
    "        0.5, 0.2, 3, 0.2, \n",
    "        0.5, 0.2, 3, 0.2, \n",
    "        0.5, 0.2, 3\n",
    "    ]} \n",
    ")\n",
    "axes = axes.flatten()\n",
    "\n",
    "dsname_map = {\n",
    "    \"linear\": \"Linear\",#\\n$(x_{t+1}=x_t+c)$\",\n",
    "    \"exp\": \"Exponential\",#\\n($x_{t+1}=x_t \\cdot c$)\",\n",
    "    \"sigmoid\": \"Sigmoid\",#\\n($x_{t+1}=x_t + c x_t(1 - x_t)$)\",\n",
    "    \"beat\": \"Beat Interference\",#\\n($x_t = x_{t - T}$)\",\n",
    "    \"linear_cos\": \"Linear + Cosine\",#\\n($x_t=x_{t-T} + c$)\",\n",
    "    \"x_times_sine\": \"Linear * Sine\",\n",
    "    \"square\": \"Quadratic\",\n",
    "    \"sine\": \"Sine\",#\\n($x_t = x_{t - T}$)\"\n",
    "    \"xsin\": \"X * Sine\",\n",
    "}\n",
    "\n",
    "result_df['ll'] = -1 * result_df['nll']\n",
    "\n",
    "ax_idx = 0\n",
    "# Iterate through the datasets and plot the samples\n",
    "# dsnames = ['linear','exp','sigmoid','square','sine','beat','linear_cos','x_times_sine']\n",
    "dsnames = ['linear','exp','sigmoid','sine','beat','x_times_sine', 'linear_cos', 'square', 'xsin']\n",
    "for idx, dsname in enumerate(dsnames):\n",
    "    \n",
    "    ax = axes[ax_idx]\n",
    "    ax_idx += 1\n",
    "\n",
    "    sns.barplot(\n",
    "        x='dataset_name',\n",
    "        # x='data_type',\n",
    "        # order=['Trend', 'Periodic', 'Trend + Periodic'],\n",
    "        y='ll',\n",
    "        hue='model_name', \n",
    "        hue_order=['TCN', 'arima',  \"text-davinci-003\"],\n",
    "        data=result_df[result_df['dataset_name'] == dsname], \n",
    "        ax=ax, \n",
    "        palette='Dark2',\n",
    "    )\n",
    "    ax.get_legend().remove()\n",
    "    if idx % 3 != 0 or idx // 2 != 1:\n",
    "        ax.set_ylabel(\"\")\n",
    "    else:\n",
    "        ax.set_ylabel(\"Log Likelihood\", fontsize=12)\n",
    "    ax.yaxis.set_label_position(\"left\")\n",
    "    # ax.yaxis.tick_right()\n",
    "    # ax.set_yticks([])\n",
    "    ax.set_ylim((-0.5,12))\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.tick_params(axis='y', which='major', pad=0)\n",
    "    \n",
    "    ax.spines['left'].set_color('black')\n",
    "    ax.plot(0, 1, '^k', transform=ax.transAxes, clip_on=False, zorder=10)\n",
    "    ax.margins(x=0.1)\n",
    "    \n",
    "    ax = axes[ax_idx]\n",
    "    ax_idx += 1\n",
    "    ax.set_axis_off()\n",
    "\n",
    "    train,test = datasets[dsname]\n",
    "    with open(f'../precomputed_outputs/synthetic/{dsname}.pkl','rb') as f:\n",
    "        data_dict = pickle.load(f)\n",
    "    \n",
    "    ax = axes[ax_idx]\n",
    "    ax_idx += 1\n",
    "    if not 'text-davinci-003' in data_dict:\n",
    "        continue\n",
    "    samples = data_dict['text-davinci-003']['samples']\n",
    "    lower = samples.quantile(0.1,axis=0)\n",
    "    upper = samples.quantile(0.9,axis=0)\n",
    "    \n",
    "    # if dsname == \"square\":\n",
    "    #     print(np.min(train.values), np.max(train.values))\n",
    "\n",
    "    pred_color = sns.color_palette('Dark2')[2]\n",
    "    ax.plot(pd.concat([train,test]),color='k',label='Ground Truth', linewidth=1)\n",
    "    ax.fill_between(samples.iloc[0].index, lower, upper, alpha=0.5)\n",
    "    ax.plot(data_dict['text-davinci-003']['median'], color=pred_color,label='GPT-3 Median', linewidth=1)\n",
    "    #ax.plot(samples.T, alpha=0.5)\n",
    "    ax.set_title(dsname_map[dsname])\n",
    "    # turn off y axis\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    if dsname == \"square\":\n",
    "        ax.set_ylim((-0.01,1))\n",
    "\n",
    "    if idx % 3 != 2:\n",
    "        ax = axes[ax_idx]\n",
    "        ax_idx += 1\n",
    "        ax.set_axis_off()\n",
    "\n",
    "handles, labels = axes[0].get_legend_handles_labels()\n",
    "handles2, labels2 = axes[2].get_legend_handles_labels()\n",
    "handles += handles2\n",
    "labels += ['TCN', 'ARIMA', 'GPT-3']\n",
    "\n",
    "plt.subplots_adjust(wspace=0, hspace=.5)\n",
    "\n",
    "plt.savefig('outputs/synthetic_qualitative.pdf', dpi=300, bbox_inches='tight')\n",
    "plt.savefig('outputs/synthetic_qualitative.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n",
    "ax.legend(\n",
    "    handles=handles[:2],\n",
    "    labels=labels[:2],\n",
    "    markerscale=1.5,\n",
    "    loc='upper left',\n",
    "    fontsize=25,\n",
    "    ncol=5,\n",
    "    frameon=True\n",
    ")\n",
    "\n",
    "plt.axis(\"off\")\n",
    "plt.savefig('outputs/synthetic_qualitative_legend_1.pdf', bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n",
    "ax.legend(\n",
    "    handles=handles[2:],\n",
    "    labels=labels[2:],\n",
    "    markerscale=1.5,\n",
    "    loc='upper left',\n",
    "    fontsize=25,\n",
    "    ncol=5,\n",
    "    frameon=True\n",
    ")\n",
    "\n",
    "plt.axis(\"off\")\n",
    "plt.savefig('outputs/synthetic_qualitative_legend_2.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
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